Blind source separation assumes that P independent sources are observed by Q sensors. The sources may be audio or sound sources, such as humans talking, music playing, etc., and the sensors may be microphones. Blind source separation finds the independent sources without knowledge of the spatial geometry of the environment and without calibration of the sensors. In conventional systems and approaches to blind source separation, all sensor signals are sent to a central location (sometimes referred to as a “fusion center”) for processing. As a result, conventional systems are not scalable; there is a limit to the number of sensors that may be implemented because (1) the transmission power required to send all the sensor signals from the sensor network to the fusion center, and (2) the fusion center has limited compute power.